code: def _evaluate(self, eval_dataset, evaluators):
      metric_results = {}
      for evaluator in evaluators:
          metric_results[evaluator.name] = []
  
      num_entries = len(eval_dataset.data)
      batch_data = {
          "user_id_input": np.zeros(self._test_batch_size, np.int32),
          "item_id_input": np.zeros(self._test_batch_size, np.int32),
      }
  
      for ind in tqdm(range(int(num_entries / self._test_batch_size))):
          entries = eval_dataset.data[
              ind
              * self._test_batch_size : (ind + 1)
              * self._test_batch_size
          ]
          user = entries["user_id"]
          item = entries["item_id"]
          labels = entries["label"]
  
          batch_data["user_id_input"][: len(user)] = user
          batch_data["item_id_input"][: len(item)] = item
  
          for evaluator in evaluators:
              results = evaluator.compute(
                  predictions=self._model.serve(batch_data)[
                      : len(user)
                  ],
                  labels=labels,
              )
              metric_results[evaluator.name].append(
                  results[: len(user)]
              )
  
      for evaluator in evaluators:
          metric_results[evaluator.name] = np.concatenate(
              metric_results[evaluator.name]
          )
  
      return metric_results
  
file_path: openrec/openrec/tf1/legacy/itr_mlp_model_trainer.py
imports: from __future__ import print_function
  from tqdm import tqdm
  import math
  from termcolor import colored
  import numpy
  import sys
label_desc: Use image node_id 3 for calling the ItrMLPModelTrainer obj's underlying member method code with example usage: obj._evaluate(eval_dataset, evaluators) and returns: metric_results